Overview

Dataset statistics

Number of variables11
Number of observations204
Missing cells3
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.7 KiB
Average record size in memory88.6 B

Variable types

Categorical1
Text1
Numeric9

Alerts

deep_poverty_rate is highly overall correlated with median_household_income and 3 other fieldsHigh correlation
in_poverty is highly overall correlated with populationHigh correlation
median_household_income is highly overall correlated with deep_poverty_rate and 4 other fieldsHigh correlation
median_rent is highly overall correlated with median_household_incomeHigh correlation
population is highly overall correlated with in_povertyHigh correlation
poverty_rate is highly overall correlated with deep_poverty_rate and 3 other fieldsHigh correlation
supplemental_poverty_measure is highly overall correlated with deep_poverty_rate and 3 other fieldsHigh correlation
unemployment_rate is highly overall correlated with deep_poverty_rate and 2 other fieldsHigh correlation
without_health_insurance is highly overall correlated with median_household_incomeHigh correlation
year is uniformly distributedUniform
in_poverty has unique valuesUnique
deep_poverty_rate has unique valuesUnique
supplemental_poverty_measure has unique valuesUnique

Reproduction

Analysis started2023-12-11 17:16:25.399453
Analysis finished2023-12-11 17:16:41.093554
Duration15.69 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

year
Categorical

UNIFORM 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2015
51 
2016
51 
2017
51 
2018
51 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters816
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2015 51
25.0%
2016 51
25.0%
2017 51
25.0%
2018 51
25.0%

Length

2023-12-11T18:16:41.244120image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T18:16:41.447567image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
2015 51
25.0%
2016 51
25.0%
2017 51
25.0%
2018 51
25.0%

Most occurring characters

ValueCountFrequency (%)
2 204
25.0%
0 204
25.0%
1 204
25.0%
5 51
 
6.2%
6 51
 
6.2%
7 51
 
6.2%
8 51
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 816
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 204
25.0%
0 204
25.0%
1 204
25.0%
5 51
 
6.2%
6 51
 
6.2%
7 51
 
6.2%
8 51
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 204
25.0%
0 204
25.0%
1 204
25.0%
5 51
 
6.2%
6 51
 
6.2%
7 51
 
6.2%
8 51
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 204
25.0%
0 204
25.0%
1 204
25.0%
5 51
 
6.2%
6 51
 
6.2%
7 51
 
6.2%
8 51
 
6.2%

state
Text

Distinct51
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-11T18:16:41.733897image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length20
Median length12
Mean length8.6666667
Min length4

Characters and Unicode

Total characters1768
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlabama
2nd rowAlaska
3rd rowArizona
4th rowArkansas
5th rowCalifornia
ValueCountFrequency (%)
new 16
 
6.3%
south 8
 
3.2%
carolina 8
 
3.2%
virginia 8
 
3.2%
dakota 8
 
3.2%
north 8
 
3.2%
florida 4
 
1.6%
georgia 4
 
1.6%
kansas 4
 
1.6%
delaware 4
 
1.6%
Other values (45) 180
71.4%
2023-12-11T18:16:42.348223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 232
13.1%
i 168
 
9.5%
n 140
 
7.9%
o 140
 
7.9%
s 124
 
7.0%
e 112
 
6.3%
r 88
 
5.0%
t 76
 
4.3%
l 60
 
3.4%
h 52
 
2.9%
Other values (36) 576
32.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1472
83.3%
Uppercase Letter 248
 
14.0%
Space Separator 48
 
2.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 232
15.8%
i 168
11.4%
n 140
9.5%
o 140
9.5%
s 124
8.4%
e 112
 
7.6%
r 88
 
6.0%
t 76
 
5.2%
l 60
 
4.1%
h 52
 
3.5%
Other values (14) 280
19.0%
Uppercase Letter
ValueCountFrequency (%)
M 36
14.5%
N 32
12.9%
C 24
9.7%
I 20
 
8.1%
D 16
 
6.5%
A 16
 
6.5%
W 16
 
6.5%
V 12
 
4.8%
O 12
 
4.8%
S 8
 
3.2%
Other values (11) 56
22.6%
Space Separator
ValueCountFrequency (%)
48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1720
97.3%
Common 48
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 232
13.5%
i 168
 
9.8%
n 140
 
8.1%
o 140
 
8.1%
s 124
 
7.2%
e 112
 
6.5%
r 88
 
5.1%
t 76
 
4.4%
l 60
 
3.5%
h 52
 
3.0%
Other values (35) 528
30.7%
Common
ValueCountFrequency (%)
48
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 232
13.1%
i 168
 
9.5%
n 140
 
7.9%
o 140
 
7.9%
s 124
 
7.0%
e 112
 
6.3%
r 88
 
5.0%
t 76
 
4.3%
l 60
 
3.4%
h 52
 
2.9%
Other values (36) 576
32.6%

population
Real number (ℝ)

HIGH CORRELATION 

Distinct203
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6113926.2
Minimum565647
Maximum38407403
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T18:16:42.716839image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum565647
5-th percentile640244.5
Q11649105
median4281544.5
Q36872925.2
95-th percentile19480001
Maximum38407403
Range37841756
Interquartile range (IQR)5223820.2

Descriptive statistics

Standard deviation6941299
Coefficient of variation (CV)1.1353259
Kurtosis8.2954818
Mean6113926.2
Median Absolute Deviation (MAD)2639955.5
Skewness2.6470128
Sum1.2472409 × 109
Variance4.8181631 × 1013
MonotonicityNot monotonic
2023-12-11T18:16:43.073317image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2881404 2
 
1.0%
4711439 1
 
0.5%
1015923 1
 
0.5%
1289255 1
 
0.5%
8783989 1
 
0.5%
2043896 1
 
0.5%
19285448 1
 
0.5%
9783738 1
 
0.5%
719722 1
 
0.5%
11289161 1
 
0.5%
Other values (193) 193
94.6%
ValueCountFrequency (%)
565647 1
0.5%
567950 1
0.5%
568943 1
0.5%
569412 1
0.5%
600127 1
0.5%
600334 1
0.5%
601538 1
0.5%
601732 1
0.5%
613291 1
0.5%
624954 1
0.5%
ValueCountFrequency (%)
38407403 1
0.5%
38242946 1
0.5%
37913144 1
0.5%
37678735 1
0.5%
27264694 1
0.5%
26794198 1
0.5%
26334005 1
0.5%
25923852 1
0.5%
20178544 1
0.5%
19858469 1
0.5%

in_poverty
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct204
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean904850.94
Minimum63311
Maximum6135142
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T18:16:43.490021image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum63311
5-th percentile74052.75
Q1228416.75
median612428.5
Q3945970.5
95-th percentile3061217.6
Maximum6135142
Range6071831
Interquartile range (IQR)717553.75

Descriptive statistics

Standard deviation1087481.3
Coefficient of variation (CV)1.2018348
Kurtosis8.492403
Mean904850.94
Median Absolute Deviation (MAD)372136
Skewness2.722568
Sum1.8458959 × 108
Variance1.1826156 × 1012
MonotonicityNot monotonic
2023-12-11T18:16:43.859506image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
887260 1
 
0.5%
136126 1
 
0.5%
104470 1
 
0.5%
938252 1
 
0.5%
420293 1
 
0.5%
2908471 1
 
0.5%
1579871 1
 
0.5%
79374 1
 
0.5%
1683890 1
 
0.5%
612714 1
 
0.5%
Other values (194) 194
95.1%
ValueCountFrequency (%)
63311 1
0.5%
63398 1
0.5%
64995 1
0.5%
65762 1
0.5%
67034 1
0.5%
68144 1
0.5%
69233 1
0.5%
69673 1
0.5%
72826 1
0.5%
72957 1
0.5%
ValueCountFrequency (%)
6135142 1
0.5%
6004257 1
0.5%
5773408 1
0.5%
5487141 1
0.5%
4472451 1
0.5%
4397307 1
0.5%
4291384 1
0.5%
4213938 1
0.5%
3180109 1
0.5%
3139258 1
0.5%

poverty_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct203
Distinct (%)100.0%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.14273572
Minimum0.078692114
Maximum0.22537674
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T18:16:44.113978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.078692114
5-th percentile0.10069063
Q10.11556657
median0.14114969
Q30.16529315
95-th percentile0.19566187
Maximum0.22537674
Range0.14668463
Interquartile range (IQR)0.049726582

Descriptive statistics

Standard deviation0.030799517
Coefficient of variation (CV)0.21578002
Kurtosis-0.55784651
Mean0.14273572
Median Absolute Deviation (MAD)0.025422791
Skewness0.33172484
Sum28.975351
Variance0.00094861027
MonotonicityNot monotonic
2023-12-11T18:16:44.374343image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1883203836 1
 
0.5%
0.1339924384 1
 
0.5%
0.08103129327 1
 
0.5%
0.106813886 1
 
0.5%
0.2056332612 1
 
0.5%
0.1508116897 1
 
0.5%
0.1614792833 1
 
0.5%
0.1102842486 1
 
0.5%
0.1491598889 1
 
0.5%
0.1620581523 1
 
0.5%
Other values (193) 193
94.6%
ValueCountFrequency (%)
0.07869211418 1
0.5%
0.08103129327 1
0.5%
0.0853328479 1
0.5%
0.08870935064 1
0.5%
0.09442021136 1
0.5%
0.09681650959 1
0.5%
0.09911998547 1
0.5%
0.09938812945 1
0.5%
0.09963418266 1
0.5%
0.1002759503 1
0.5%
ValueCountFrequency (%)
0.2253767406 1
0.5%
0.2231360852 1
0.5%
0.2145261638 1
0.5%
0.2100923972 1
0.5%
0.2090161968 1
0.5%
0.2075166061 1
0.5%
0.2056332612 1
0.5%
0.2001062971 1
0.5%
0.1976156994 1
0.5%
0.1970418363 1
0.5%

median_household_income
Real number (ℝ)

HIGH CORRELATION 

Distinct203
Distinct (%)100.0%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean59212.424
Minimum40593
Maximum85203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T18:16:44.644113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum40593
5-th percentile44803.1
Q151620
median56990
Q365642
95-th percentile77290.5
Maximum85203
Range44610
Interquartile range (IQR)14022

Descriptive statistics

Standard deviation10231.562
Coefficient of variation (CV)0.17279417
Kurtosis-0.57082014
Mean59212.424
Median Absolute Deviation (MAD)6743
Skewness0.48597003
Sum12020122
Variance1.0468486 × 108
MonotonicityNot monotonic
2023-12-11T18:16:44.888419image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44765 1
 
0.5%
63870 1
 
0.5%
73381 1
 
0.5%
80088 1
 
0.5%
46744 1
 
0.5%
64894 1
 
0.5%
52752 1
 
0.5%
61843 1
 
0.5%
54021 1
 
0.5%
50051 1
 
0.5%
Other values (193) 193
94.6%
ValueCountFrequency (%)
40593 1
0.5%
41754 1
0.5%
41995 1
0.5%
42019 1
0.5%
43385 1
0.5%
43469 1
0.5%
43529 1
0.5%
44097 1
0.5%
44334 1
0.5%
44717 1
0.5%
ValueCountFrequency (%)
85203 1
0.5%
83242 1
0.5%
82372 1
0.5%
81740 1
0.5%
80776 1
0.5%
80212 1
0.5%
80088 1
0.5%
79835 1
0.5%
78945 1
0.5%
77765 1
0.5%

deep_poverty_rate
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct204
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.063762558
Minimum0.035375777
Maximum0.10114825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T18:16:45.124152image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.035375777
5-th percentile0.046264795
Q10.052629755
median0.062585247
Q30.072514403
95-th percentile0.091035695
Maximum0.10114825
Range0.065772475
Interquartile range (IQR)0.019884648

Descriptive statistics

Standard deviation0.013911338
Coefficient of variation (CV)0.21817409
Kurtosis-0.20460457
Mean0.063762558
Median Absolute Deviation (MAD)0.0099527891
Skewness0.54362969
Sum13.007562
Variance0.00019352534
MonotonicityNot monotonic
2023-12-11T18:16:45.375867image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08173129271 1
 
0.5%
0.05676414453 1
 
0.5%
0.03598512319 1
 
0.5%
0.04767105241 1
 
0.5%
0.09140631422 1
 
0.5%
0.06724520996 1
 
0.5%
0.07033283189 1
 
0.5%
0.0540125215 1
 
0.5%
0.06874177806 1
 
0.5%
0.0702198037 1
 
0.5%
Other values (194) 194
95.1%
ValueCountFrequency (%)
0.0353757773 1
0.5%
0.03598512319 1
0.5%
0.03663267823 1
0.5%
0.0393316564 1
0.5%
0.04386251919 1
0.5%
0.04458991133 1
0.5%
0.04515309688 1
0.5%
0.04524041563 1
0.5%
0.0456792419 1
0.5%
0.04597755209 1
0.5%
ValueCountFrequency (%)
0.1011482524 1
0.5%
0.1004548422 1
0.5%
0.09857089127 1
0.5%
0.09811602134 1
0.5%
0.09765837099 1
0.5%
0.09523839179 1
0.5%
0.09373678258 1
0.5%
0.09266924274 1
0.5%
0.09236665371 1
0.5%
0.09201243793 1
0.5%

median_rent
Real number (ℝ)

HIGH CORRELATION 

Distinct170
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean929.22549
Minimum643
Maximum1566
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T18:16:45.629011image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum643
5-th percentile698.8
Q1775
median870.5
Q31056.25
95-th percentile1324.6
Maximum1566
Range923
Interquartile range (IQR)281.25

Descriptive statistics

Standard deviation202.4514
Coefficient of variation (CV)0.21787112
Kurtosis0.21181392
Mean929.22549
Median Absolute Deviation (MAD)124
Skewness0.95008251
Sum189562
Variance40986.57
MonotonicityNot monotonic
2023-12-11T18:16:45.883064image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
764 3
 
1.5%
717 2
 
1.0%
766 2
 
1.0%
984 2
 
1.0%
808 2
 
1.0%
711 2
 
1.0%
1173 2
 
1.0%
789 2
 
1.0%
988 2
 
1.0%
709 2
 
1.0%
Other values (160) 183
89.7%
ValueCountFrequency (%)
643 1
0.5%
655 1
0.5%
658 1
0.5%
675 1
0.5%
676 1
0.5%
677 1
0.5%
681 1
0.5%
689 1
0.5%
690 1
0.5%
696 1
0.5%
ValueCountFrequency (%)
1566 1
0.5%
1507 1
0.5%
1487 1
0.5%
1456 1
0.5%
1438 1
0.5%
1429 1
0.5%
1424 1
0.5%
1362 1
0.5%
1358 1
0.5%
1357 1
0.5%

unemployment_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.053117647
Minimum0.026
Maximum0.089
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T18:16:46.117905image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.026
5-th percentile0.035
Q10.043
median0.054
Q30.061
95-th percentile0.073
Maximum0.089
Range0.063
Interquartile range (IQR)0.018

Descriptive statistics

Standard deviation0.012384079
Coefficient of variation (CV)0.23314434
Kurtosis-0.48418824
Mean0.053117647
Median Absolute Deviation (MAD)0.009
Skewness0.11533338
Sum10.836
Variance0.0001533654
MonotonicityNot monotonic
2023-12-11T18:16:46.368768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.055 13
 
6.4%
0.058 10
 
4.9%
0.049 9
 
4.4%
0.042 9
 
4.4%
0.053 8
 
3.9%
0.038 7
 
3.4%
0.06 6
 
2.9%
0.052 6
 
2.9%
0.054 6
 
2.9%
0.036 6
 
2.9%
Other values (43) 124
60.8%
ValueCountFrequency (%)
0.026 1
 
0.5%
0.028 2
 
1.0%
0.029 1
 
0.5%
0.03 1
 
0.5%
0.032 3
1.5%
0.033 1
 
0.5%
0.034 1
 
0.5%
0.035 6
2.9%
0.036 6
2.9%
0.037 1
 
0.5%
ValueCountFrequency (%)
0.089 1
 
0.5%
0.08 1
 
0.5%
0.079 2
1.0%
0.077 1
 
0.5%
0.076 2
1.0%
0.075 2
1.0%
0.074 1
 
0.5%
0.073 4
2.0%
0.072 2
1.0%
0.071 3
1.5%

without_health_insurance
Real number (ℝ)

HIGH CORRELATION 

Distinct203
Distinct (%)100.0%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.10208393
Minimum0.028044639
Maximum0.20581292
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T18:16:46.624488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.028044639
5-th percentile0.047832471
Q10.074641424
median0.099397772
Q30.12464434
95-th percentile0.16405268
Maximum0.20581292
Range0.17776828
Interquartile range (IQR)0.050002918

Descriptive statistics

Standard deviation0.035731833
Coefficient of variation (CV)0.35002408
Kurtosis-0.26922003
Mean0.10208393
Median Absolute Deviation (MAD)0.024939995
Skewness0.33914427
Sum20.723037
Variance0.0012767639
MonotonicityNot monotonic
2023-12-11T18:16:46.886660image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1265654916 1
 
0.5%
0.06706339024 1
 
0.5%
0.07454701017 1
 
0.5%
0.09670640859 1
 
0.5%
0.1249509171 1
 
0.5%
0.07576346398 1
 
0.5%
0.1205052636 1
 
0.5%
0.08049182823 1
 
0.5%
0.07412405639 1
 
0.5%
0.1485121067 1
 
0.5%
Other values (193) 193
94.6%
ValueCountFrequency (%)
0.02804463874 1
0.5%
0.0298336149 1
0.5%
0.03212920213 1
0.5%
0.03560218161 1
0.5%
0.0401745326 1
0.5%
0.04058641029 1
0.5%
0.04097942142 1
0.5%
0.04604539619 1
0.5%
0.04657997447 1
0.5%
0.04707656325 1
0.5%
ValueCountFrequency (%)
0.205812919 1
0.5%
0.1931657728 1
0.5%
0.1829333864 1
0.5%
0.1824884248 1
0.5%
0.1818038283 1
0.5%
0.1796967714 1
0.5%
0.1737894338 1
0.5%
0.1712114231 1
0.5%
0.1691501955 1
0.5%
0.1665611407 1
0.5%

supplemental_poverty_measure
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct204
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13473891
Minimum0.072964701
Maximum0.21995879
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T18:16:47.150092image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.072964701
5-th percentile0.095253498
Q10.11055648
median0.13149748
Q30.15368371
95-th percentile0.194108
Maximum0.21995879
Range0.14699408
Interquartile range (IQR)0.043127227

Descriptive statistics

Standard deviation0.030041155
Coefficient of variation (CV)0.22295827
Kurtosis-0.31564125
Mean0.13473891
Median Absolute Deviation (MAD)0.021351043
Skewness0.48843642
Sum27.486739
Variance0.00090247099
MonotonicityNot monotonic
2023-12-11T18:16:47.425619image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1849565898 1
 
0.5%
0.1162105962 1
 
0.5%
0.07696216331 1
 
0.5%
0.099949384 1
 
0.5%
0.1965353463 1
 
0.5%
0.1407747101 1
 
0.5%
0.1470009817 1
 
0.5%
0.103213113 1
 
0.5%
0.1397020783 1
 
0.5%
0.1582871777 1
 
0.5%
Other values (194) 194
95.1%
ValueCountFrequency (%)
0.07296470124 1
0.5%
0.07638684579 1
0.5%
0.07696216331 1
0.5%
0.08233001568 1
0.5%
0.0883526903 1
0.5%
0.08952867876 1
0.5%
0.09025331218 1
0.5%
0.09283893707 1
0.5%
0.09325254311 1
0.5%
0.09493958627 1
0.5%
ValueCountFrequency (%)
0.2199587859 1
0.5%
0.2083615922 1
0.5%
0.2043766701 1
0.5%
0.2019761475 1
0.5%
0.1983197422 1
0.5%
0.1976638242 1
0.5%
0.1974591904 1
0.5%
0.1966586614 1
0.5%
0.1965353463 1
0.5%
0.1959504924 1
0.5%

Interactions

2023-12-11T18:16:38.809616image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:25.700394image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:28.216324image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:29.703617image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:31.291722image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:32.759534image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:34.267930image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:35.812943image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:37.310979image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:38.986926image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:25.886963image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:28.386734image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:29.889033image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:31.461215image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:32.931703image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:34.437630image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:36.010674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:37.488136image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:39.151874image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:26.051723image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:28.550239image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:30.057389image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:31.617832image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:33.096332image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:34.591066image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:36.185122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:37.647363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:39.321322image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:26.219476image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:28.725374image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:30.230478image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:31.782048image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:33.258270image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:34.758268image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:36.359742image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:37.817676image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:39.475727image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:26.374473image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:28.880267image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:30.385966image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:31.923743image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:33.401026image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:34.901207image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:36.509696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:37.967957image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:39.640714image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:26.541882image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:29.044623image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:30.558635image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:32.082506image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:33.600030image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:35.064107image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:36.667132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:38.139009image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:39.800785image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:26.763175image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:29.187824image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:30.712900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:32.230574image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:33.759478image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:35.277560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:36.818976image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:38.299296image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:39.968794image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:26.994833image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:29.349649image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:30.893881image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:32.391201image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:33.923629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:35.473700image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:36.973191image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:38.466831image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:40.192245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:27.183650image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:29.517752image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:31.120600image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:32.558028image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:34.097365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:35.642735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:37.141780image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-11T18:16:38.634611image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-12-11T18:16:47.628009image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
deep_poverty_ratein_povertymedian_household_incomemedian_rentpopulationpoverty_ratesupplemental_poverty_measureunemployment_ratewithout_health_insuranceyear
deep_poverty_rate1.0000.435-0.729-0.4000.2280.9740.9620.6040.4370.000
in_poverty0.4351.000-0.2040.0800.9680.4200.3790.3390.2720.000
median_household_income-0.729-0.2041.0000.814-0.006-0.802-0.800-0.281-0.5150.000
median_rent-0.4000.0800.8141.0000.215-0.476-0.4780.096-0.2510.000
population0.2280.968-0.0060.2151.0000.2070.1640.2160.1330.000
poverty_rate0.9740.420-0.802-0.4760.2071.0000.9810.5660.4940.000
supplemental_poverty_measure0.9620.379-0.800-0.4780.1640.9811.0000.6060.4910.000
unemployment_rate0.6040.339-0.2810.0960.2160.5660.6061.0000.4120.192
without_health_insurance0.4370.272-0.515-0.2510.1330.4940.4910.4121.0000.102
year0.0000.0000.0000.0000.0000.0000.0000.1920.1021.000

Missing values

2023-12-11T18:16:40.442309image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T18:16:40.755619image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-11T18:16:40.983743image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

yearstatepopulationin_povertypoverty_ratemedian_household_incomedeep_poverty_ratemedian_rentunemployment_ratewithout_health_insurancesupplemental_poverty_measure
02015Alabama47114398872600.18832044765.00.0817317170.0720.1265650.184957
12015Alaska716218729570.10186473355.00.04524011460.0790.1818040.103407
22015Arizona648891711806900.18195551492.00.0854339130.0690.1503610.173725
32015Arkansas28724975536440.19274041995.00.0796536770.0580.1416200.190663
42015California3767873561351420.16282864500.00.07063412550.0730.1470190.153437
52015Colorado51617226539690.12669663909.00.05664810020.0520.1231590.114905
62015Connecticut34833033663510.10517371346.00.04898110750.0690.0790230.105405
72015Delaware9008201083150.12024061255.00.05864510180.0580.0823730.124256
82015Florida1922820831801090.16538849426.00.07258010020.0700.1796970.157022
92015Georgia973714617889470.18372451244.00.0833618790.0710.1712110.170468
yearstatepopulationin_povertypoverty_ratemedian_household_incomedeep_poverty_ratemedian_rentunemployment_ratewithout_health_insurancesupplemental_poverty_measure
1942018Tennessee648878610465080.16127952375.00.0695198410.0550.1009330.153114
1952018Texas2726469442139380.15455760629.00.0657509980.0490.1737890.148913
1962018Utah29979633099040.10337271414.00.0459789880.0320.0999350.090253
1972018Vermont600334670340.11166160782.00.0494029720.0380.0409790.109791
1982018Virginia81621078935800.10947972577.00.05218912020.0420.0921900.107047
1992018Washington71617088216210.11472474073.00.05265611940.0430.0678770.102562
2002018West Virginia17765013154640.17757644097.00.0764817110.0580.0648940.178040
2012018Wisconsin56282136682200.11872760773.00.0513328370.0320.0577310.110469
2022018Wyoming567950633110.11147361584.00.0488218430.0390.1134540.110586
2032018District of Columbia6509121094970.16822185203.00.09201214870.0750.0401750.161510